AI-DRIVEN ASSURANCE: A CONCEPTUAL FRAMEWORK FOR INTELLIGENT SOFTWARE QUALITY TESTING

Abstract

This study represents a conceptual foundation for forthcoming experimental validation using state-of-the-art AI assurance models. The continuous evolution of artificial intelligence (AI) has triggered a paradigm shift in software quality assurance (SQA), transitioning it from manual, static testing practices into intelligent, adaptive, and data-driven assurance ecosystems. Traditional SQA methods, reliant on fixed test case design and human-intensive validation, struggle to manage the complexity and velocity of modern software systems. Recent advances in large language models (LLMs) such as GPT-4o, CodeBERT, and TestGPT demonstrate promising potential in automating test generation and improving analysis precision; however, they primarily operate at isolated testing stages and lack unified reasoning and assurance governance. This research introduces the AI-Driven Assurance Framework (AIDAF)—a comprehensive architecture that integrates explainable AI reasoning, semantic test generation, adaptive validation, and governance-driven feedback loops into a cohesive assurance system. AIDAF redefines quality assurance as a continuous learning process, where AI and human oversight collaborate to ensure transparency and reliability throughout the software lifecycle. Conceptual validation, supported by simulation of test generation and performance evaluation, achieved balanced outcomes with precision = 0.89, recall = 0.84, and F1-score = 0.86. These results confirm AIDAF’s effectiveness in producing explainable, consistent, and self-improving assurance results. The study contributes to the evolution of intelligent assurance by transforming testing from a reactive verification activity into a proactive, reasoning-driven, and continuously adaptive process. Future research directions include standardization of assurance intelligence benchmarks and governance models for trustworthy AI-based quality engineering.